I would like to use a filtered deck to study all different subjects I have in Anki at once, using other decks to mainly control review/new cards flow of a particular topic (via deck options).
My problem is that search is:due only finds cards that are to be reviewed today (as opposed to learnt) and is:new matches all unseen cards.
So, what search term would return all cards that are scheduled for today, including review and new ones?
Studying default deck proved unsatisfactory as the order of cards was predictable, very much so in AnkiDroid.
For Anki Version < 2.0
(is:new or is:due)
This search will give you a filtered deck that includes cards due today as well as all new cards.
If I'm understanding your question correctly, you want to have a filtered deck that includes all cards due today as well as only the number of new cards that you want to use from the deck settings. I have looked for a way to do this for some time but have not yet found a way to do so. my solution is to use a deck to randomize the order of my reviews:
is:due
(Selected by: Random)
If you then put this filtered deck and your deck with new cards in the same parent deck, you can study using the parent deck to mix the new cards and reviews.
Edit: 2020-06, for Anki Version > 2.1
Anki Scheduler V2.1 now supports this directly by allowing decks with two criteria.
Related
I'm currently trying to learn the construction of class diagrams for an upcoming exam, although I'm having difficulties knowing how to identify classes based on a use case specification (or any similar description of a system).
I understand people here can be reluctant to help with education based questions, I just wanted to clarify that I'm not asking for the work to be done for me, I just need to be pointed in the right direction.
I'm going over a past revision paper which contains a use case specification for the process of 'purchasing an ice cream' at a vending machine. The first question is to identify 9 typical classes from this specification.
I understand a class is like an object and usually identified by a noun, although my confusion is how I am able to extract 9 classes as I can only seem to find 6, that is if they're even correct:
Customer, Student, Staff, Touch Screen, Change Dispenser, and Member Card.
Here is the use case specification, apologies for the length:
Ben & Jerry’s company has just installed a new vending machine at a
University; it has a variety of ice creams available, in the format of
tubs or mini-tubs of different flavours. Products are subsidised for
students and staff but not for the rest of users; such that a customer
can insert a member card in a card reader and the subsidised prize for
the selected ice cream is displayed. You have designed a use case
model of the system, and identified a single use case (“purchase an
ice cream”) where the main actors are the customers.
ID: VM1
Name: Purchase a tub
Main Actors:
Subsidised customer (staff and students) 2 Non-subsidised customer
Pre-conditions:
Machine is on and works perfectly fine
There is stock of drinks and coins
There is sufficient stock of coins in the machine for providing change
There is sufficient stock of products
Main Flow:
Machine displays a welcome message
Student/staff inserts member card
System validates member card
Customer selects the tub by pressing the touch screen
Machine displays the subsidised price for the selected ice cream
REPEAT until sufficient coins entered: 6.1 Customer enters coin 6.2 Touch screen displays the amount entered so far
System dispenses the tub
IF too many coins are entered change is delivered
Price is added to the weekly total amount
Machine resets
Post Condition: Ice cream purchased
Alternative flow 1:
IF customer does not have sufficient coins 6.1 Customer presses the return button 6.2 System returns the entered coins
Alternative flow 2:
IF validation is unsuccessful 3.1 Message informing user 3.2 Non-subsidised price is show 3.3 Use case continues main flow 4
If anybody can help I'd really appreciate it. Thanks
You have mostly identified a number of actors, few classes itself. To start your design, ask yourself what the system under consideration (SUC) is. Obviously a vending machine. Now look at the UC how this SUC acts. There are a couple of hints:
control resources (temperature, ice cream, tubs, etc.),
control collection of money,
control dispension of ice cream.
For these you can create controller classes which look to the outside and act accordingly (read temp -> turn on cooling; count money -> start vending; etc.)
The vending process with customer interaction is probably even more complex (show offers, make selection, pricing, etc.)
As you already guessed, this is no tutorial point. However, those are the basic steps to start the design. You can go on and create sequence diagrams to verify collaboration between the single classes (lots of exceptions like: temp controller starting to yell when temp does go up too much).
I am looking for an algorithm or method that would help identify general phrases from a corpus of text that has a particular dialect (it is from a specific domain but for my case is a dialect of the English language) -- for example the following fragment could be from a larger corpus related to the World or Warcraft or perhaps MMORPHs.
players control a character avatar within a game world in third person or first person view, exploring the landscape, fighting various monsters, completing quests, and interacting with non-player characters (NPCs) or other players. Also similar to other MMORPGs, World of Warcraft requires the player to pay for a subscription, either by buying prepaid game cards for a selected amount of playing time, or by using a credit or debit card to pay on a regular basis
As output from the above I would like to identify the following general phrases:
first person
World of Warcraft
prepaid game cards
debit card
Notes:
There is a previous questions similar to mine here and here but for clarification mine has the following differences:
a. I am trying to use an existing toolkit such as NLTK, OpenNLP, etc.
b. I am not interested in identifying other Parts of Speech in the sentence
c. I can use human intervention where the algorithm presents the identified noun phrases to a human expert and the human expert can then confirm or reject the findings however we do not have resources for training a model of language on hand-annotated data
Nltk has built in part of speech tagging that has proven pretty good at identifying unknown words. That said, you seem to misunderstand what a noun is and you should probably solidify your understanding of both parts of speech, and your question.
For instance, in first person first is an adjective. You could automatically assume that associated adjectives are a part of that phrase.
Alternately, if you're looking to identify general phrases my suggestion would be to implement a simple Markov Chain model and then look for especially high transition probabilities.
If you're looking for a Markov Chain implementation in Python I would point you towards this gist that I wrote up back in the day: https://gist.github.com/Slater-Victoroff/6227656
If you want to get much more advanced than that, you're going to quickly descend into dissertation territory. I hope that helps.
P.S. Nltk includes a huge number of pre-annotated corpuses that might work for your purposes.
It appears you are trying to do noun phrase extraction. The TextBlob Python library includes two noun phrase extraction implementations out of the box.
The simplest way to get started is to use the default FastNPExtractor which is based of Shlomi Babluki's algorithm described here.
from text.blob import TextBlob
text = '''
players control a character avatar within a game world in third person or first
person view, exploring the landscape, fighting various monsters, completing quests,
and interacting with non-player characters (NPCs) or other players. Also similar
to other MMORPGs, World of Warcraft requires the player to pay for a
subscription, either by buying prepaid game cards for a selected amount of
playing time, or by using a credit or debit card to pay on a regular basis
'''
blob = TextBlob(text)
print(blob.noun_phrases) # ['players control', 'character avatar' ...]
Swapping out for the other implementation (an NLTK-based chunker) is quite easy.
from text.np_extractors import ConllExtractor
blob = TextBlob(text, np_extractor=ConllExtractor())
print(blob.noun_phrases) # ['character avatar', 'game world' ...]
If neither of these suffice, you can create your own noun phrase extractor class. I recommend looking at the TextBlob np_extractor module source for examples. To gain a better understanding of noun phrase chunking, check out the NLTK book, Chapter 7.
Given "violence" as input would it be possible to come up with how violence construed by a person (e.g. physical violence, a book, an album, a musical group ..) as mentioned below in Ref #1.
Assuming if the user meant an Album, what would be the best way to look for violence as an album from a set of tweets.
Is there a way to infer this via any of the NLP API(s) say OpenNLP.
Ref #1
violence/N1 - intentional harmful physical action.
violence/N2 - the property of being wild or turbulent.
Violence/N6 - a book from Neil L. Whitehead; nonfiction
Violence/N7 - an album by The Last Resort
Violence/N8 - Violence is the third album by the Washington-based Alternative metal music group Nothingface.
Violence/N9 - a musical group which produced the albums Eternal Nightmare and Nothing to Gain
Violence/N10 - a song by Aesthetic Perfection, Angel Witch, Arsenic, Beth Torbert, Brigada Flores Magon, etc on the albums A Natural Disaster, Adult Themes for Voice, I Bificus, Retribution, S.D.E., etc
Violence/N11 - an album by Bombardier, Dark Quarterer and Invisible Limits
Violence/N12 - a song by CharlElie Couture, EsprieM, Fraebbblarnir, Ian Hunter, Implant, etc on the albums All the Young Dudes, Broke, No Regrets, Power of Limits, Repercussions, etc
Violence/N18 - Violence: The Roleplaying Game of Egregious and Repulsive Bloodshed is a short, 32-page roleplaying game written by Greg Costikyan under the pseudonym "Designer X" and published by Hogshead Publishing as part of its New Style line of games.
Violence/N42 - Violence (1947) is an American drama film noir directed by Jack Bernhard.
Pure automatic inference is a little to hard in general for this problem.
Instead we might use :
Resources like WordNet, or a semantics dictionary.
For languages other than English you can look at eurowordnet (non free) dataset.
To get more meaning (i.e. for the album sense) we process some well managed resource like Wikipedia. Wikipedia as a lot of meta information that would be very useful for this kind of processing.
The reliability of the process is achieve just by combining the maximum number of data source and processing them correctly, with specialized programs.
As a last resort you may try hand processing/annotating. Long and costly, but useful in enterprise context where you need only a small part of a language.
No free lunch here.
If you're working on English NLP in python, then you can try the wordnet API as such:
from nltk.corpus import wordnet as wn
query = 'violence'
for ss in wn.synsets(query):
print query, str(ss.offset).zfill(8)+'-'+ss.pos, ss.definition
If you're working on other human languages, maybe you can take a look at the open wordnets available from http://casta-net.jp/~kuribayashi/multi/
NOTE: the reason for str(ss.offset).zfill(8)+'-'+ss.pos, it's because it is used as the unique id for each sense of a specific word. And this id is consistent across the open wordnets for every language. the first 8 digits gives the id and the character after the dash is the Part-of-Speech of the sense.
Check this out: Twitter Filtering Demo from Idilia. It does exactly what you want by first analyzing a piece of text to discover the meaning of its words and then filtering the texts that contain the sense that you are looking for. It's available as an API.
Disclaimer: I work for Idilia.
You can extract all contexts "violence" occurs in (context can be a whole document, or a window of say 50 words), then convert them into features (using say bag of words), then cluster these features. As clustering is unsupervised, you won't have names for the clusters, but you can label them with some typical context.
Then you need to see which cluster "violence" in the query belongs to. Either based on other words in the query which act as a context or by asking explicitly (Do you mean violence as in "...." or as in "....")
This will be incredibly difficult due to the fact that the proper noun uses of the word 'Violence' will be incredibly infrequent as a proportion of all words and their frequency distribution is likely highly skewed in some way. We run into these problems almost any time we want to do some form of Named Entity Disambiguation.
No tool I'm aware of will do this for you, so you will be building your own classifier. Using Wikipedia as a training resource as Mr K suggested is probably your best bet.
my graduate project is about Smart Attendance System for University using RFID.
What if one student have multiple cards (cheating) and he want to attend his friend as well? The situation here my system will not understand the human adulteration and it will attend the detected RFID Tags by the reader and the result is it will attend both students and it will store them in the database.
I am facing this problem from begging and it is a huge glitch in my system.
I need a solution or any idea for this problem and it can be implemented in the code or in the real live to identify the humans.
There are a few ways you could do this depending upon your dedication, the exact tech available to you, and the consistency of the environment you are working with. Here are the first two that come to mind:
1) Create a grid of reader antennae on the ceiling of your room and use signal response times to the three nearest readers to get a decent level of confidence as to where the student tag is. If two tags register as being too close, display the associated names for the professor to call out and confirm presence. This solution will be highly dependent upon the precision of your equipment and stability of temperature/humidity in the room (and possibly other things like liquid and metal presence).
2) Similar to the first solution, but a little different. Some readers and tags (Impinj R2000 and Indy Readers, Impinj Monza 5+ for sure, maybe others aswell) have the ability to report a response time and a phase angle associated with the signal received from an interrogated tag. Using a set up similar to the first, you can get a much higher level of reliability and precision if you use this method.
Your software could randomly pick a few names of attending people, so that the professor can ask them to identify themselves. This will not eliminate the possibility of cheating, but increase the risk of beeing caught.
Other idea: count the number of attendiees (either by the prof or by camera + SW) and compare that to the number of RfID tags visible.
There is no solution for this RFID limitation.
But if you could then you can use Biometric(fingerprint) recognition facility with RFID card. With this in your system you have to:
Integrate biometric scanner with your RFID reader
Store biometric data in your card
and while making attendance :
Read UID
Scan biometric by student
Match scanned biometric with your stored biometric(in the card :
step 2)
Make attendance (present if biometric matched, absent if no match)
Well, We all have that glitch, and you can do nothing about it, but with the help of a camera system, i think it would minimise this glitch.
why use a camera system and not a biometric fingerprint system? lets re-phrase the question, why use RFID if there is biometric fingerprint system ? ;)
what is ideal to use, is an RFID middleware that handle the tag reading.
once the reader detects a tag, the middleware simply call the security camera system and request for a snapshot, and store it in the db. I'm using an RFID middleware called Envoy.
Okay, I've seen lots of posts about fuzzy string matching, Levenstein distance, longest common substring, and so on. None of them seem to fit exactly for what I want to do. I'm pulling product results from a variety of web services, and from those I can build a big list of names for the product. These names may include a bunch of variable junk. Here are some examples, from SearchUPC:
Apple 60W magsafe adapter L-shape with extension cord
Original Apple 60W Power Adapter (L-shaped Connector) for MacBook MC461LL/A with AC Extension Wall Cord (Bulk Packaging)
Current Apple MagSafe 60W Power Adapter for MacBook MC461LL/A with AC Extension Wall Cord (Bulk Packaging)
Apple 60W MagSafe Power Adapter - Apple Mac Accessories
Apple - MagSafe 60W Power Adapter for MacBook and 13\" MacBook Pro
MagSafe - power adapter - 60 Watt
etc. What I'd like to do is pull the common product name (which to my heuristic human eye is obviously Apple 60W MagSafe Power Adapter), but none of the aforementioned methods seem likely to work. My main problem is that I don't know what to search the list of strings for... At first, I was thinking of trying longest common substring, but it seems like that will fail as a bunch of the strings have things out of order, which might yield a product name of power adapter, which is not terribly useful to the user.
Note: the vast majority of the records returned from the SearchUPC API (mostly omitted here) do include the literal string "Apply 60W MagSafe Power Adapter".
I'm implementing this in Objective-C, for iOS, but I'm really interested in the algorithm more than the implementation, so any language is acceptable.
If you want to compare strings but need something more robust than longest common substring with respect to changed order of substrings, you might look into a technique called string tiling. Simplified, the principle is as follows:
Find the largest common substring (larger than a minimum length) in both strings
Remove that substring from both strings
Repeat until there are no more substrings larger than your minimal length
In practice, the ratio between the remaining (unmatched) stringparts to the initial lengths is a excellent indicator on how well the strings match. And the technique is extremely robust against reordering of substrings. You can find the scientific paper by M. Wise describing this technique here. I have implemented the algorithm myself in the past (it's not hard), but apparantly free implementations are readily available (for example here). While I have used the algorithm in a variety of fuzzy matching scenarios, I can't vouch for the implementation which I have never used myself.
String tiling does not solve the problem of finding the largest common productname in itself, but it seems to me that by slightly modifying the algorithm, you can do so. You're probably less interested in calculating the matchpercentage than in the keeping the similar parts?
As the previous poster remarked, such fuzzy matching almost always needs some kind of manual validation, as both false positives and false negatives are unavoidable.
Sounds like your approach needs to be two fold. One should be matching records with one another. The other part is pulling the "canonical name" out of those matching records. In your case it is a product, but same concept. I'm not sure how you will go about associating groups of matching records with the standardized product name, but I would suggest trying to pull the important information out of the records and trying to match that to some resources on the Internet. For instance, for your example, maybe you compare your data to an apple product list. Or you could try and have a robot that crawls google and pulls the top result to try and associate. Bottom line, at the end of the day, if your text is really dirty, you will need to include some human intervention. What I mean is that you may set a threshold for match, no match, and needs review. Good luck.